Spatially Adaptive Regularization in Image Segmentation

Antonelli, Laura; Simone, Valentina De; Serafino, Daniela di

doi:10.3390/a13090226

Cited by 13 publications

(18 citation statements)

References 33 publications

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“…Here λ > 0 is a regularization parameter that needs careful tuning to suitably balance F and R (see, e.g., [53] and the references therein).…”

Section: Regularized Segmentation Modelsmentioning

confidence: 99%

“…This is the case, for example, of methods based on forward-backward splitting techniques, such as proximal-gradient methods [86,87], and the forward-backward Expectation Maximization (EM) method in [88]. ADMM and split Bregman methods do not use smooth approximations too [53,[89][90][91][92][93]. The difficulties associated with the non-differentiability of the TV functional may be also overcome by reformulating the minimization problem as a saddle-point problem and solving it by a primal-dual algorithm such as the Chambolle-Pock one [94,95].…”

Section: First Discretize Then Optimizementioning

confidence: 99%

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A view of computational models for image segmentation

Antonelli¹,

Simone²,

Serafino³

2021

Preprint

Self Cite

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Image segmentation is a central topic in image processing and computer vision and a key 1 issue in many applications, e.g., in medical imaging, microscopy, document analysis and remote 2 sensing. According to the human perception, image segmentation is the process of dividing an 3 image into non-overlapping regions. These regions, which may correspond to different objects, are 4 fundamental for the correct interpretation and classification of the scene represented by the image. 5The division into regions is not unique, but it depends on the application, i.e., it must be driven by 6 the final goal of the segmentation and hence by the most significant features with respect to that 7 goal. Image segmentation is an inherently ill-posed problem. A classical approach to deal with ill 8 posedness consists in the use of regularization, which allows us to incorporate in the model a-priori 9 information about the solution. In this work we provide a brief overview of regularized mathematical 10 models for image segmentation, considering edge-based and region-based variational models, as well 11 as statistical and machine-learning approaches. We also sketch numerical methods that are applied in 12 computing solutions coming from those techniques.

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“…Here λ > 0 is a regularization parameter that needs careful tuning to suitably balance F and R (see, e.g., [53] and the references therein).…”

Section: Regularized Segmentation Modelsmentioning

confidence: 99%

Section: First Discretize Then Optimizementioning

confidence: 99%

A view of computational models for image segmentation

Antonelli¹,

Simone²,

Serafino³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The paper [4] introduces spatially adaptive regularization in a variational segmentation model, where the segmentation of images is improved by taking into account their smooth and nonsmooth regions in an appropriate way. Three techniques were introduced, based on the application of spatial filters and thresholding.…”

Section: Special Issuementioning

confidence: 99%

2020 Selected Papers from Algorithms’ Editorial Board Members

Werner

2021

Algorithms

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“…Chen et al [20] integrated the transmission map and the saliency map into a unified level set formulation to extract the salient target contours of the underwater images. Antonelli et al [21] proposed some spatially varying regularization methods by using local image features (such as textures, edge, noise, etc.) to prevent excessive regularization in smooth regions and preserve spatial features in nonsmooth regions.…”

Section: Introductionmentioning

confidence: 99%

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Yang

Feng

2022

Applied Sciences

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

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Spatially Adaptive Regularization in Image Segmentation

Cited by 13 publications

References 33 publications

A view of computational models for image segmentation

A view of computational models for image segmentation

2020 Selected Papers from Algorithms’ Editorial Board Members

Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

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